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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Ling Ma, Yidong Jiang, Zhi He, Weilei Bao, Zhongyun Fu, Gang Xu, Wenju Xiao, Chunxia School of Computer Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial Systems Wuhan University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Computing Hong Kong Polytechnic University Hong Kong Amazon Palo Alto United States

ISBN: (纸本)9798400706868

Recently, learning-based methods have made significant progress for image specular highlight removal. However, many of these approaches treat all the image pixels uniformly, overlooking the negative impact of invalid pixels on feature reconstruction. This oversight often leads to undesirable outcomes, such as color distortion or residual highlights. In this paper, we propose a novel image specular highlight removal network called HighlightRNet, which utilizes valid pixels as references to reconstruct the highlight-free image. To achieve this, we introduce a context-aware fusion block (CFBlock) that aggregates information in four directions, effectively capturing global contextual information. Additionally, we introduce a location-aware feature transformation module (LFTModule) to adaptively learn the valid pixels for feature reconstruction, thereby avoiding information errors caused by invalid pixels. With these modules, our method can produce high-quality highlight-free results without color distortion and highlight residual. Furthermore, we develop a multiple light image-capturing system to construct a large-scale highlight dataset called NSH, which exhibits minimal misalignment in image pairs and minimal brightness variation in non-highlight regions. Experimental results on various datasets demonstrate the superiority of our method over state-of-the-art methods, both qualitatively and quantitatively. © 2024 ACM.

关键词： Pixels

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The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy

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computer Modeling in Engineering & Sciences 2022年第5期131卷 567-597页

作者： Dandan Xu Haijian Shao Xing Deng Xia Wang School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Information Science and Technology Nantong UniversityNantong226019China

As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly *** current state of research in renewable generation and power forecasting technology,such as wind and photovoltaic power(PV),is described in this paper,with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power *** methods for forecasting wind power and PV *** physical model,statistical learningmethod,andmachine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV ***,the experiments demonstrated that cloud map identification has a significant impact on PV *** a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes,this paper summarizes the classification of wind power and PV generation systems,as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

关键词： Deep learning wind power forecasting PV generation and forecasting hidden-layer information analysis topology optimization

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CAMG: Context-Aware Moment Graph Network for Multimodal Temporal Activity Localization via Language 12th

CAMG: Context-Aware Moment Graph Network for Multimodal Tem...

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12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Hu, Yuelin Xu, Yuanwu Zhang, Yuejie Feng, Rui Zhang, Tao Lu, Xuequan Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai200433 China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai200433 China School of Information Technology Deakin University Waurn PondsVIC3216 Australia

ISBN: (纸本)9783031446924

Temporal Activity Localization via Language (TALL) is a challenging task for language based video understanding, especially when a video contains multiple moments of interest and the language query has words describing complex context dependencies between the moments. Latest studies have proposed various ways to exploit the temporal context of adjacent moments, but two apparent limitations remained. First, only limited context information was encoded based on RNNs or 2-D convolutions, which highly depended on the pre-sorting of proposals and lacked flexibility. Second, semantically correlated content in different moments was ignored, i.e., semantic context. To address these limitations, we propose a novel GCN-based framework, i.e., Context-Aware Moment Graph (CAMG) network, to jointly model the temporal context and semantic context. Also, we design a multi-step fusion scheme to aggregate object, motion and textual features. A Query-Gated Integration Module is further designed to select queried objects and filter out noisy ones. Our model achieves superior performance to state-of-the-art methods on two widely-used benchmark datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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A Policy-Controlled Signature Scheme for end-to-end Secure Communication 12

A Policy-Controlled Signature Scheme for end-to-end Secure C...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Yu, Feng Meng, Linghui Xi, Jing Li, Xianxian Lu, Qianhui Zhu, Weidong Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology GuiLin China University of SouthEast Jiangsu Provincial Key Laboratory of Netwrok and Information Security Nanjing China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security GuiLin China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University GuiLin China

ISBN: (纸本)9798331511074

In the digitalized society, end-to-end security communication is receiving increasing attention. Issues such as ma-licious sender impersonation and over-privilege communication continue to prevail in end-to-end communication. This paper pro-poses a policy-controlled signature scheme aimed at addressing these concerns through identity verification and permission man-agement, thus establishing a secure mode of communication. The scheme designs policy-controlled signatures with partial policy hiding functionality, employing expressive access structures and garbled Bloom Filter (GBF) to protect the sender's attributes. In comparison to related approaches, this scheme provides benefits in terms of signature computation cost, thereby enhancing the resilience and efficiency of the communication system against attacks. © 2024 IEEE.

关键词： Authentication Protocol

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Towards DDIs Identification by Knowledge Graph with BiRW and Back Aggregation

Towards DDIs Identification by Knowledge Graph with BiRW and...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Yin, Zhuang Lin, Xiaoli Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9781665468190

Effective identification of potential drug-drug interactions (DDIs) can prevent adverse effects caused by DDIs to a certain extent. This paper proposes the new hybrid method for predicting DDIs, which is called BiRW-KGBAN that combines bidirectional random walk and back aggregation based on knowledge graph. BiRW-KGBAN first constructs two knowledge graph KG-DrugBank and KG-KEGG that integrate data related to drugs. Then, an improved sampling method based on bidirectional random walk (BiRW) is used to sample the neighborhood information of drugs, including directly connected entities, related semantic relations and potential information. In addition, the path with drug as the center node is also extracted. Finally, two improved back aggregation model KGBAN1 and KGBAN2 are used to obtain the final embedded representation of the drugs. The experiments show that, compared with other existing methods, our method could obtain the higher-order topological information and the deeper potential neighborhood information of drugs, and has great improvements in DDIs prediction. The case study also shows that the proposed method has the potential for actual DDIs prediction. © 2022 IEEE.

关键词： Knowledge graph

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Dynamic Fusion Nearest Neighbor Machine Translation via Dempster-Shafer Theory 1

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18th China Conference on Machine Translation, CCMT 2022

作者： Yang, Zongheng Hou, Hongxu Sun, Shuo Wu, Nier Wang, Yisong Jian, Weichen Wang, Pengcong College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (数字)9789811979606

ISBN: (纸本)9789811979590

kNN-MT has been recently proposed, uses a token-level k-nearest neighbor approach to retrieve similar sentences, obtaining knowledge guidance from an external memory module, and then combined with the prediction results of the translation model, which greatly improves the accuracy of machine translation. However, kNN-MT uses simple linear interpolation in the fusion of retrieval probability and translation probability, which can not dynamically adjust the fusion ratio according to the matching degree of the retrieved sentences. Moreover, different fusion ratios need to be explored in different translation scenarios, and the translation effect will be affected when the retrieved sentences have a low matching degree or contain noise. In this paper, we propose an approach via Dempster-Shafer theory (DST) to dynamically fuse different probability distributions to suit different scenarios. We demonstrate that our approach is more significantly improved and more robust than the traditional kNN-MT, and we explore the application of kNN-MT in low-resource translation scenarios for the first time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine translation

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A Belief Propagation Cooperative Localization Algorithm in GNSS-Loss Environments 3

A Belief Propagation Cooperative Localization Algorithm in G...

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3rd International Conference on Digital Signal and computer Communications, DSCC 2023

作者： Ma, Fei Tan, Guoping Zhou, Siyuan Hu, Hexuan College of Computer and Information Hohai University Jiangsu Intelligent Transportation and Intelligent Driving Research Institute Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University College of Computer and Information Hohai University Nanjing China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

When vehicles are in driving environments such as urban roads and tunnels, the global navigation satellite system (GNSS) signal can fail to achieve positioning due to occlusion. Cooperative positioning can effectively improve the accuracy and coverage of vehicle positioning. In this paper, we address the vehicle localization issue in GNSS-Loss driving environments and with random packet loss due to wireless communications. We propose an improved cooperative localization algorithm based on belief propagation, which first linearizes the measurement model of distance by statistical linear regression method, then performs belief propagation based on the linearized model, and solves the packet loss problem by expanding the dimension of the vehicle state. From the simulation results, the algorithm can effectively reduce the impact of packet loss and improve the vehicle localization accuracy. © 2023 SPIE.

关键词： Belief propagation

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Monotonic and nonmonotonic gentzen deduction systems for L_(3)-valued propositional logic

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Frontiers of computer Science 2021年第3期15卷 123-135页

作者： Cungen CAO Lanxi HU Yuefei SUI Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China

A sequent is a pair(Г,△),which is true under an as-signment if either some formula inГis false,or some formula in △ is *** L3-valued propositional logic,a mulisequent is a triple △|Θ|Г,which is true under an assignment if either some formula in △ has truth-value t,or some formula in Θ has truth-value m,or some formula in Г has truth-value￡.Corre-spondingly there is a sound and complete Gentzen deduction system G for multisequents which is ***,a CO-multisequent is a triple △:Θ:Г,which is valid if there is an assignment v in which each formula in△has truth-value≠t,each formula in Θ has truth-value≠m,and each formula in Г has truth-value≠￡.Correspondingly there is a sound and com-plete Gentzen deduction system G-for co-multisequents which is nonmonotonic.

关键词： three-valued logic multisequent co-multise-quent monotonicity Gentzen deduction system

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Graph Contrastive Learning with Learnable Graph Augmentation 48

Graph Contrastive Learning with Learnable Graph Augmentation

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Pu, Xinyan Zhang, Ke Shu, Huazhong Coatrieux, Jean Louis Kong, Youyong Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Nanjing China Universit de Rennes 1 Ltsi Rennes France Centre de Recherche en Information BioMdicale Sino-Franais Rennes France

ISBN: (纸本)9781728163277

Graph contrastive learning has gained popularity due to its success in self-supervised graph representation learning. Augmented views in contrastive learning greatly determine the quality of the learned representations. Handcrafted data augmentations in previous work require tedious trial-and- errors per dataset, which is time-consuming and resource-intensive. Here, we propose a Graph Contrastive learning framework with Learnable graph Augmentation called GraphCLA. Specifically, learnable graph augmentation trains augmented views by minimizing the mutual information (MI) between the input graphs and their augmented graphs. This paper designs a kernel contrastive loss function based on an end-to-end differentiable graph kernel to learn augmented views. In addition, this paper utilizes a min-max optimization strategy to learn challenging augmented graphs and to learn discriminative representations adversarially. Finally, we compared GraphCLA with state-of-the-art self-supervised learning baselines and experimentally validate the effectiveness of GraphCLA. © 2023 IEEE.

关键词： Contrastive learning data augmentation graph representation learning

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Convertible certificateless proxy re-signcryption for data access control in cloud computing

Convertible certificateless proxy re-signcryption for data a...

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2023 International Conference on computer Application and information Security, ICCAIS 2023

作者： Wu, Chenhuang Yang, Linghui Yuan, Beibei Li, Huimin Huang, Hui Fujian Key Laboratory of Financial Information Processing Putian University Fujian Putian351100 China Fujian Zhongxin Network Security Information Technology Co. Ltd. Fujian Fuzhou350011 China School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China

ISBN: (纸本)9781510675032

Cloud storage service enables users not only to save their local data storage costs but also to access their outsourced data at any time and anywhere. Traditionally, a data owner outsources the encrypted data to the cloud server and then checks its integrity with the help of cloud auditing strategies. Obviously, in such a manner, only confidentiality and integrity are considered following the above two independent steps. Furthermore, it becomes an issue that the data owner may want to share their outsourced data with some authorized data receivers and convince them that the data are from and the same as the data owner's. To solve this issue, absorbing the advantage of the certificateless cryptosystem, we propose an efficient certificateless proxy re-signcryption (CLPRSC) for data access control in cloud computing. Moreover, the CLPRSC possesses ciphertext verifiable, convertible, and designated re-signcryptable properties with the highest efficiency among the achievable signcryption with proxy re-encryption schemes, which may be of independent interest. © 2024 SPIE.

关键词： Access control

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